import pandas as pd
import numpy as np
def read_all(data_folder):
"""
Read all of the data and return a series of pandas data frames
Example usage:
df_cgm, df_bolus, df_basal, df_insulin = read_all(r"data")
Args:
data_folder (str): path to the root folder containing all the data files. The file names
and structure should be left exactly as they were in the initial download from Glooko.
Returns:
A 4-element tuple
- **df_cgm** (pandas df): pandas dataframe containing CGM time series data
- **df_bolus** (pandas df): Bolus data
- **df_basal** (pandas df): Basal data
- **df_insulin** (pandas df): Insulin corrections data
"""
# Load and format CGM data
df_cgm_data = pd.read_csv(data_folder + r"/cgm_data.csv", header=1,\
names=["time", "bg", "sn_cgm"])
df_cgm_data["time"] = pd.to_datetime(df_cgm_data["time"])
# Load and format bolus data
df_bolus_data = pd.read_csv(data_folder + r"/Insulin data/bolus_data.csv", header=1, \
names=["time", "insulin_type", "bg_input", "carbs_input", "carb_ratio",\
"insulin_delivered", "initial_delivery", "extended_delivery", "sn_omni"])
df_bolus_data["time"] = pd.to_datetime(df_bolus_data["time"])
# Load and format basal data
df_basal_data = pd.read_csv(data_folder + r"/Insulin data/basal_data.csv", header=1,\
names=["time", "insulin_type", "duration", "percentage", "rate", "insulin_delivered",\
"sn_omni"])
df_basal_data["time"] = pd.to_datetime(df_basal_data["time"])
# Load and format insulin data
df_insulin_data = pd.read_csv(data_folder + r"/Insulin data/insulin_data.csv", header=1,\
names=["time", "total_bolus", "total_insulin", "total_basal", "sn_omni"])
df_insulin_data["time"] = pd.to_datetime(df_insulin_data["time"])
return df_cgm_data, df_bolus_data, df_basal_data, df_insulin_data
